Advertisement

Computational Visual Media

, Volume 5, Issue 1, pp 3–20 | Cite as

BING: Binarized normed gradients for objectness estimation at 300fps

  • Ming-Ming ChengEmail author
  • Yun Liu
  • Wen-Yan Lin
  • Ziming Zhang
  • Paul L. Rosin
  • Philip H. S. Torr
Open Access
Research Article
  • 198 Downloads

Abstract

Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g., add, bitwise shift, etc.). To improve localization quality of the proposals while maintaining efficiency, we propose a novel fast segmentation method and demonstrate its effectiveness for improving BING’s localization performance, when used in multi-thresholding straddling expansion (MTSE) post-processing. On the challenging PASCAL VOC2007 dataset, using 1000 proposals per image and intersection-over-union threshold of 0.5, our proposal method achieves a 95.6% object detection rate and 78.6% mean average best overlap in less than 0.005 second per image.

Keywords

object proposals objectness visual attention category agnostic proposals 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Nos. 61572264, 61620106008).

References

  1. [1]
    Alexe, B.; Deselaers, T.; Ferrari, V. Measuring the objectness of image windows. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 34, No. 11, 2189–2202, 2012.CrossRefGoogle Scholar
  2. [2]
    Alexe, B.; Deselaers, T.; Ferrari, V. What is an object? In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 73–80, 2010.Google Scholar
  3. [3]
    Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580–587, 2014.Google Scholar
  4. [4]
    Girshick, R. Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, 1440–1448, 2015.Google Scholar
  5. [5]
    He, K.; Zhang, X.; Ren, S.; Sun, J. Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Computer Vision–ECCV 2014. Lecture Notes in Computer Science, Vol. 8691. Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T. Eds. Springer Cham, 346–361, 2014.Google Scholar
  6. [6]
    Wang, N.; Li, S.; Gupta, A.; Yeung, D.-Y. Transferring rich feature hierarchies for robust visual tracking. arXiv preprint arXiv:1501.04587, 2015.Google Scholar
  7. [7]
    Kwak, S.; Cho, M.; Laptev, I.; Ponce, J.; Schmid, C. Unsupervised object discovery and tracking in video collections. In: Proceedings of the IEEE International Conference on Computer Vision, 3173–3181, 2015.Google Scholar
  8. [8]
    Kading, C.; Freytag, A.; Rodner, E.; Bodesheim, P.; Denzler, J. Active learning and discovery of object categories in the presence of unnameable instances. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4343–4352, 2015.Google Scholar
  9. [9]
    Cho, M.; Kwak, S.; Schmid, C.; Ponce, J. Unsupervised object discovery and localization in the wild: Partbased matching with bottom-up region proposals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1201–1210, 2015.Google Scholar
  10. [10]
    Arbeláez, P.; Hariharan, B.; Gu, C.; Gupta, S.; Bourdev, L.; Malik, J. Semantic segmentation using regions and parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3378–3385, 2012.Google Scholar
  11. [11]
    Carreira, J.; Caseiro, R.; Batista, J.; Sminchisescu, C. Semantic segmentation with second-order pooling. In: Computer Vision–ECCV 2012. Lecture Notes in Computer Science, Vol. 7578. Fitzgibbon, A.; Lazebnik, S.; Perona, P.; Sato, Y.; Schmid, C. Eds. Springer Berlin Heidelberg, 430–443, 2012.Google Scholar
  12. [12]
    Sun, J.; Ling, H. Scale and object aware image retargeting for thumbnail browsing. In: Proceedings of the International Conference on Computer Vision, 1511–1518, 2011.Google Scholar
  13. [13]
    Sener, F.; Bas, C.; Ikizler-Cinbis, N. On recognizing actions in still images via multiple features. In: Computer Vision–ECCV 2012. Workshops and Demonstrations. Lecture Notes in Computer Science, Vol. 7585. Fusiello, A.; Murino, V.; Cucchiara, R. Eds. Springer Berlin Heidelberg, 263–272, 2012.Google Scholar
  14. [14]
    Teuber, H.-L. Physiological psychology. Annual Review of Psychology Vol. 6, 267–296, 1955.CrossRefGoogle Scholar
  15. [15]
    Wolfe, J. M.; Horowitz, T. S. What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience Vol. 5, 495–501, 2004.CrossRefGoogle Scholar
  16. [16]
    Koch, C.; Ullman, S. Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurbiology Vol. 4, No. 4, 219–227, 1985.Google Scholar
  17. [17]
    Desimone, R.; Duncan, J. Neural mechanisms of selective visual attention. Annual Review of Neuroscience Vol. 18, 193–222, 1995.CrossRefGoogle Scholar
  18. [18]
    Forsyth, D. A.; Malik, J.; Fleck, M. M.; Greenspan, H.; Leung, T.; Belongie, S.; Carson, C.; Bregler, C. Finding pictures of objects in large collections of images. In: Object Representation in Computer Vision II. Lecture Notes in Computer Science, Vol. 1144. Ponce, J.; Zisserman, A.; Hebert, M. Eds. Springer Berlin Heidelberg, 335–360, 1996.Google Scholar
  19. [19]
    Heitz, G.; Koller, D. Learning spatial context: Using stuff to find things. In: Computer Vision–ECCV 2008. Lecture Notes in Computer Science, Vol. 5302. Forsyth, D.; Torr, P.; Zisserman, A. Eds. Springer Berlin Heidelberg, 30–43, 2008.Google Scholar
  20. [20]
    Uijlings, J. R. R.; van de Sande, K. E. A.; Gevers, T.; Smeulders, A. W. M. Selective search for object recognition. International Journal on Computer Vision Vol. 104, No. 2, 154–171, 2013.CrossRefGoogle Scholar
  21. [21]
    Endres, I.; Hoiem, D. Category-independent object proposals with diverse ranking. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 36, No. 2, 222–234, 2014.CrossRefGoogle Scholar
  22. [22]
    Cheng, M.-M.; Zhang, Z.; Lin, W.-Y.; Torr, P. H. S. BING: Binarized normed gradients for objectness estimation at 300fps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3286–3293, 2014.Google Scholar
  23. [23]
    Wei, Y.; Xia, W.; Lin, M.; Huang, J.; Ni, B.; Dong, J.; Zhao, Y.; Yan, S. HCP: A flexible CNN framework for multi-label image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 38, No. 9, 1901–1907, 2016.CrossRefGoogle Scholar
  24. [24]
    Zha, S.; Luisier, F.; Andrews, W.; Srivastava, N.; Salakhutdinov, R. Exploiting image-trained CNN architectures for unconstrained video classification. In: Proceedings of the British Machine Vision Conference, 2015.Google Scholar
  25. [25]
    Pinheiro, P. O.; Collobert, R. From image-level to pixel-level labeling with convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1713–1721, 2015.Google Scholar
  26. [26]
    Wu, J.; Yu, Y.; Huang, C.; Yu, K. Deep multiple instance learning for image classification and autoannotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3460–3469, 2015.Google Scholar
  27. [27]
    Lee, Y. J.; Grauman, K. Predicting important objects for egocentric video summarization. International Journal on Computer Vision Vol. 114, No. 1, 38–55, 2015.MathSciNetCrossRefGoogle Scholar
  28. [28]
    Paisitkriangkrai, S.; Shen, C.; Hengel, A. v. d. Pedestrian detection with spatially pooled features and structured ensemble learning. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 38, No. 6, 1243–1257, 2016.CrossRefGoogle Scholar
  29. [29]
    Zhang, D.; Han, J.; Li, C.; Wang, J.; Li, X. Detection of co-salient objects by looking deep and wide. International Journal on Computer Vision Vol. 120, No. 2, 215–232, 2016.MathSciNetCrossRefGoogle Scholar
  30. [30]
    Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 39, No. 6, 1137–1149, 2015.CrossRefGoogle Scholar
  31. [31]
    Redmon, J.; Farhadi, A. YOLO9000: Better, faster, stronger. arXiv preprint arXiv:1612.08242, 2016.Google Scholar
  32. [32]
    Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.-Y.; Berg, A. C. SSD: Single shot multibox detector. In: Computer Vision–ECCV 2016. Lecture Notes in Computer Science, Vol. 9905. Leibe, B.; Matas, J.; Sebe, N.; Welling, M. Eds. Springer Cham, 21–37, 2016.Google Scholar
  33. [33]
    Everingham, M.; Van Gool, L.; Williams, C. K. I.; Winn, J.; Zisserman, A. The PASCAL visual object classes (VOC) challenge. International Journal on Computer Vision Vol. 88, No. 2, 303–338, 2010.CrossRefGoogle Scholar
  34. [34]
    Zitnick, C. L.; Dollár, P. Edge boxes: Locating object proposals from edges. In: Computer Vision–ECCV 2014. Lecture Notes in Computer Science, Vol. 8693. Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T. Eds. Springer Cham, 391–405, 2014.Google Scholar
  35. [35]
    Hosang, J.; Benenson, R.; Dollár, P.; Schiele, B. What makes for effective detection proposals? IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 38, No. 4, 814–830, 2016.CrossRefGoogle Scholar
  36. [36]
    Pont-Tuset, J.; Arbeláez, P.; Barron, J. T.; Marques, F.; Malik, J. Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 39, No. 1, 128–140, 2017.CrossRefGoogle Scholar
  37. [37]
    Zhao, Q.; Liu, Z.; Yin, B. Cracking BING and beyond. In: Proceedings of the British Machine Vision Conference, 2014.Google Scholar
  38. [38]
    Chen, X.; Ma, H.; Wang, X.; Zhao, Z. Improving object proposals with multi-thresholding straddling expansion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2587–2595, 2015.Google Scholar
  39. [39]
    Ren, C. Y.; Prisacariu, V. A.; Reid, I. D. gSLICr: SLIC superpixels at over 250Hz. arXiv preprint arXiv:1509.04232, 2015.Google Scholar
  40. [40]
    Achanta, R.; Shaji, A.; Smith, K.; Lucchi, A.; Fua, P.; SÃijsstrunk, S. SLIC superpixels compared to stateof-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 34, No. 11, 2274–2282, 2012.CrossRefGoogle Scholar
  41. [41]
    Felzenszwalb, P. F.; Huttenlocher, D. P. Efficient graphbased image segmentation. International Journal on Computer Vision Vol. 59, No. 2, 167–181, 2004.CrossRefGoogle Scholar
  42. [42]
    Cheng, M.-M.; Liu, Y.; Hou, Q.; Bian, J.; Torr, P.; Hu, S.-M.; Tu, Z. HFS: Hierarchical feature selection for efficient image segmentation. In: Computer Vision–ECCV 2016. Lecture Notes in Computer Science, Vol. 9907. Leibe, B.; Matas, J.; Sebe, N.; Welling, M. Eds. Springer Cham, 867–882, 2016.Google Scholar
  43. [43]
    Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; Zitnick, C. L. Microsoft COCO: Common objects in context. In: Computer Vision–ECCV 2014. Lecture Notes in Computer Science, Vol. 8693. Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T. Eds. Springer Cham, 740–755, 2014.Google Scholar
  44. [44]
    Zhang, Z.; Warrell, J.; Torr, P. H. S. Proposal generation for object detection using cascaded ranking SVMs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1497–1504, 2011.Google Scholar
  45. [45]
    Rahtu, E.; Kannala, J.; Blaschko, M. B. Learning a category independent object detection cascade. In: Proceedings of the International Conference on Computer Vision, 1052–1059, 2011.Google Scholar
  46. [46]
    Manen, S.; Guillaumin, M.; Van Gool, L. Prime object proposals with randomized Prim’s algorithm. In: Proceedings of the IEEE International Conference on Computer Vision, 2536–2543, 2013.Google Scholar
  47. [47]
    Rantalankila, P.; Kannala, J.; Rahtu, E. Generating object segmentation proposals using global and local search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2417–2424, 2014.Google Scholar
  48. [48]
    Krähenbühl, P.; Koltun, V. Geodesic object proposals. In: Computer Vision–ECCV 2014. Lecture Notes in Computer Science, Vol. 8693. Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T. Eds. Springer Cham, 725–739, 2014.Google Scholar
  49. [49]
    Krähenbühl, P.; Koltun, V. Learning to propose objects. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1574–1582, 2015.Google Scholar
  50. [50]
    Humayun, A.; Li, F.; Rehg, J. M. RIGOR: Reusing inference in graph cuts for generating object regions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 336–343, 2014.Google Scholar
  51. [51]
    Borji, A.; Cheng, M. M.; Jiang, H. et al. Salient object detection: A survey. arXiv preprint arXiv:1411.5878, 2014.zbMATHGoogle Scholar
  52. [52]
    Judd, T.; Durand, F.; Torralba, A. A benchmark of computational models of saliency to predict human fixations. Technical Report. MIT Tech Report, 2012.Google Scholar
  53. [53]
    Itti, L.; Koch, C.; Niebur, E. A model of saliencybased visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 20, No. 11, 1254–1259, 1998.CrossRefGoogle Scholar
  54. [54]
    Ma, Y.-F.; Zhang, H.-J. Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the 11th ACM International Conference on Multimedia, 374–381, 2003.Google Scholar
  55. [55]
    Harel, J.; Koch, C.; Perona, P. Graph-based visual saliency. In: Proceedings of the 19th International Conference on Neural Information Processing Systems, 545–552, 2006.Google Scholar
  56. [56]
    Borji, A.; Sihite, D. N.; Itti, L. Quantitative analysis of human-model agreement in visual saliency modeling: A comparative study. IEEE Transactions on Image Processing Vol. 22, No. 1, 55–69, 2013.MathSciNetCrossRefzbMATHGoogle Scholar
  57. [57]
    Li, Y.; Hou, X.; Koch, C.; Rehg, J. M.; Yuille, A. L. The secrets of salient object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 280–287, 2014.Google Scholar
  58. [58]
    Borji, A.; Cheng, M.-M.; Jiang, H.; Li, J. Salient object detection: A benchmark. IEEE Transactions on Image Processing Vol. 24, No. 12, 5706–5722, 2015.MathSciNetCrossRefzbMATHGoogle Scholar
  59. [59]
    Liu, T.; Sun, J.; Zheng, N.; Tang, X.; Shum, H. Learning to detect a salient object. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–8, 2007.Google Scholar
  60. [60]
    Achanta, R.; Hemami, S.; Estrada, F.; Susstrunk, S. Frequency-tuned salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1597–1604, 2009.Google Scholar
  61. [61]
    Cheng, M.-M.; Mitra, N. J.; Huang, X.; Torr, P. H. S.; Hu, S.-M. Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 37, No. 3, 569–582, 2015.CrossRefGoogle Scholar
  62. [62]
    Perazzi, F.; Krähenbühl, P.; Pritch, Y.; Hornung, A. Saliency filters: Contrast based filtering for salient region detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 733–740, 2012.Google Scholar
  63. [63]
    Cheng, M.-M.; Zheng, S.; Lin, W.-Y.; Vineet, V.; Sturgess, P.; Crook, N.; Mitra, N. J.; Torr, P. ImageSpirit: Verbal guided image parsing. ACM Transactions on Graphics Vol. 34, No. 1, Article No. 3, 2014.Google Scholar
  64. [64]
    Zheng, S.; Cheng, M.-M.; Warrell, J.; Sturgess, P.; Vineet, V.; Rother, C.; Torr, P. H. S. Dense semantic image segmentation with objects and attributes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3214–3221, 2014.Google Scholar
  65. [65]
    Li, K.; Zhu, Y.; Yang, J.; Jiang, J. Video superresolution using an adaptive superpixel-guided autoregressive model. Pattern Recognition Vol. 51, 59–71, 2016.CrossRefGoogle Scholar
  66. [66]
    Zhang, G.-X.; Cheng, M.-M.; Hu, S.-M.; Martin, R. R. A shape-preserving approach to image resizing. Computer Graphics Forum Vol. 28, No. 7, 1897–1906, 2009.CrossRefGoogle Scholar
  67. [67]
    Zheng, Y.; Chen, X.; Cheng, M.-M.; Zhou, K.; Hu, S.-M.; Mitra, N. J. Interactive images: Cuboid proxies for smart image manipulation. ACM Transactions on Graphics Vol. 31, No. 4, Article No. 99, 2012.Google Scholar
  68. [68]
    Chen, T.; Cheng, M.-M.; Tan, P.; Shamir, A.; Hu, S.-M. Sketch2Photo: Internet image montage. ACM Transactions on Graphics Vol. 28, No. 5, Article No. 124, 2009.Google Scholar
  69. [69]
    Huang, H.; Zhang, L.; Zhang, H.-C. Arcimboldo-like collage using internet images. ACM Transactions on Graphics Vol. 30, No. 6, Article No. 155, 2011.Google Scholar
  70. [70]
    Chia, A. Y.-S.; Zhuo, S.; Gupta, R. K.; Tai, Y.-W.; Cho, S.-Y.; Tan, P.; Lin, S. Semantic colorization with internet images. ACM Transactions on Graphics Vol. 30, No. 6, Article No. 156, 2011.Google Scholar
  71. [71]
    He, J.; Feng, J.; Liu, X.; Cheng, T.; Lin, T.-H.; Chung, H.; Chang, S.-F. Mobile product search with bag of hash bits and boundary reranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3005–3012, 2012.Google Scholar
  72. [72]
    Chen, T.; Tan, P.; Ma, L.-Q.; Cheng, M.-M.; Shamir, A.; Hu, S.-M. PoseShop: Human image database construction and personalized content synthesis. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 5, 824–837, 2013.CrossRefGoogle Scholar
  73. [73]
    Hu, S.-M.; Chen, T.; Xu, K.; Cheng, M.-M.; Martin, R. R. Internet visual media processing: A survey with graphics and vision applications. The Visual Computer Vol. 29, No. 5, 393–405, 2013.CrossRefGoogle Scholar
  74. [74]
    Cheng, M.-M.; Mitra, N. J.; Huang, X.; Hu, S.-M. SalientShape: Group saliency in image collections. The Visual Computer Vol. 30, No. 4, 443–453, 2014.CrossRefGoogle Scholar
  75. [75]
    Carreira, J.; Sminchisescu, C. CPMC: Automatic object segmentation using constrained parametric min-cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 34, No. 7, 1312–1328, 2012.CrossRefGoogle Scholar
  76. [76]
    Lu, C.; Liu, S.; Jia, J.; Tang, C.-K. Contour box: Rejecting object proposals without explicit closed contours. In: Proceedings of the IEEE International Conference on Computer Vision, 2021–2029, 2015.Google Scholar
  77. [77]
    Fan, R.-E.; Chang, K.-W.; Hsieh, C.-J.; Wang, X.-R.; Lin, C.-J. LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research Vol. 9, 1871–1874, 2008.zbMATHGoogle Scholar
  78. [78]
    Gottlieb, J. P.; Kusunoki, M.; Goldberg, M. E. The representation of visual salience in monkey parietal cortex. Nature Vol. 391, No. 6666, 481–484, 1998.CrossRefGoogle Scholar
  79. [79]
    Hare, S.; Saffari, A.; Torr, P. H. S. Efficient online structured output learning for keypoint-based object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1894–1901, 2012.Google Scholar
  80. [80]
    Zheng, S.; Sturgess, P.; Torr, P. H. S. Approximate structured output learning for constrained local models with application to real-time facial feature detection and tracking on low-power devices. In: Proceedings of the 10th IEEE International Conference andWorkshops on Automatic Face and Gesture Recognition, 1–8, 2013.Google Scholar
  81. [81]
    Viola, P.; Jones, M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, I–I, 2001.Google Scholar
  82. [82]
    Chavali, N.; Agrawal, H.; Mahendru, A.; Batra, D. Object-proposal evaluation protocol is ‘gameable’. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 835–844, 2016.Google Scholar
  83. [83]
    Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.Google Scholar
  84. [84]
    Dalal, N.; Triggs, B. Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, 886–893, 2005.Google Scholar
  85. [85]
    Felzenszwalb, P. F.; Girshick, R. B.; McAllester, D.; Ramanan, D. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 32, No. 9, 1627–1645, 2010.CrossRefGoogle Scholar
  86. [86]
    Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; Fei-Fei, L. ImageNet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 248–255, 2009.Google Scholar
  87. [87]
    He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778, 2016.Google Scholar
  88. [88]
    Kuo, W.; Hariharan, B.; Malik, J. DeepBox: Learning objectness with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, 2479–2487, 2015.Google Scholar
  89. [89]
    Zhang, Z.; Liu, Y.; Chen, X.; Zhu, Y.; Cheng, M.-M.; Saligrama, V.; Torr, P. H. Sequential optimization for efficient high-quality object proposal generation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 40, No. 5, 1209–1223, 2018.CrossRefGoogle Scholar
  90. [90]
    Chen, W.; Xiong, C.; Xu, R.; Corso, J. J. Actionness ranking with lattice conditional ordinal random fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,748–755, 2014.Google Scholar

Copyright information

© The Author(s) 2018

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (https://doi.org/creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from https://doi.org/www.springer.com/journal/41095. To submit a manuscript, please go to https://doi.org/www.editorialmanager.com/cvmj.

Authors and Affiliations

  • Ming-Ming Cheng
    • 1
    Email author
  • Yun Liu
    • 1
  • Wen-Yan Lin
    • 2
  • Ziming Zhang
    • 3
  • Paul L. Rosin
    • 4
  • Philip H. S. Torr
    • 5
  1. 1.CCSNankai UniversityTianjinChina
  2. 2.Institute for Infocomm ResearchSingaporeSingapore
  3. 3.MERLCambridgeUSA
  4. 4.Cardiff UniversityWalesUK
  5. 5.University of OxfordOxfordUK

Personalised recommendations